
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase     :	Dict       			=  {
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[int]       			=  ['EfficientFormerImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[int]       			=  [
        'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'EfficientFormerForImageClassification',
        'EfficientFormerForImageClassificationWithTeacher',
        'EfficientFormerModel',
        'EfficientFormerPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Dict       			=  [
        'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFEfficientFormerForImageClassification',
        'TFEfficientFormerForImageClassificationWithTeacher',
        'TFEfficientFormerModel',
        'TFEfficientFormerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_efficientformer import EfficientFormerImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_efficientformer import (
            EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            EfficientFormerForImageClassification,
            EfficientFormerForImageClassificationWithTeacher,
            EfficientFormerModel,
            EfficientFormerPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_efficientformer import (
            TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFEfficientFormerForImageClassification,
            TFEfficientFormerForImageClassificationWithTeacher,
            TFEfficientFormerModel,
            TFEfficientFormerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	Optional[Any]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					int     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      None:







    """simple docstring"""






    warnings.warn(
        """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
        """ use BeitImageProcessor instead."""     ,				UpperCamelCase__     ,				)
    super().__init__(*UpperCamelCase__     ,				**UpperCamelCase__       )

